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008			220510s2022 sz | s |||| 0|eng d
020			_a9783030930882
024	7		_a10.1007/978-3-030-93088-2 _2doi
040			_aTR-AnTOB _beng _erda _cTR-AnTOB
041			_aeng
050		4	_aQA76.9.D343
072		7	_aTJK _2bicssc
072		7	_aTEC041000 _2bisacsh
072		7	_aTJK _2thema
090			_aQA76.9.D343EBK
100	1		_aChakraborty, Sanjay. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aData Classification and Incremental Clustering in Data Mining and Machine Learning _h[electronic resource] / _cby Sanjay Chakraborty, Sk Hafizul Islam, Debabrata Samanta.
250			_a1st ed. 2022.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aEAI/Springer Innovations in Communication and Computing, _x2522-8609
505	0		_aIntroduction to Data Mining & Knowledge Discovery -- A Brief Concept on Machine Learning -- Supervised Learning based Data Classification and Incremental Clustering -- Data Classification and Incremental Clustering using Unsupervised Learning -- Research Intention towards Incremental Clustering -- Applications and Trends in Data Mining & Machine Learning -- Feature subset selection techniques with Machine Learning -- Data Mining Based variant subsets features.
520			_aThis book is a comprehensive, hands-on guide to the basics of data mining and machine learning with a special emphasis on supervised and unsupervised learning methods. The book lays stress on the new ways of thinking needed to master machine learning based on the Python, R, and Java programming platforms. This book first provides an understanding of data mining, machine learning and their applications, giving special attention to classification and clustering techniques. The authors offer a discussion on data mining and machine learning techniques with case studies and examples. The book also describes the hands-on coding examples of some well-known supervised and unsupervised learning techniques using three different and popular coding platforms: R, Python, and Java. This book explains some of the most popular classification techniques (K-NN, Naïve Bayes, Decision tree, Random forest, Support vector machine etc,) along with the basic description of artificial neural network and deep neural network. The book is useful for professionals, students studying data mining and machine learning, and researchers in supervised and unsupervised learning techniques. Provides a comprehensive review of various data mining techniques and architecture, primarily focusing on supervised and unsupervised learning Presents hands-on coding examples using three popular coding platforms: R, Python, and Java Includes case-studies, examples, practice problems, questions, and solutions for students and professionals, focusing on machine learning and data science.
650		0	_aTelecommunication.
650		0	_aComputational intelligence.
650		0	_aComputer vision.
650		0	_aData mining.
650	1	4	_aCommunications Engineering, Networks.
650	2	4	_aComputational Intelligence.
650	2	4	_aComputer Vision.
650	2	4	_aData Mining and Knowledge Discovery.
653		0	_aMachine learning
700	1		_aIslam, Sk Hafizul. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aSamanta, Debabrata. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
830		0	_aEAI/Springer Innovations in Communication and Computing, _x2522-8609
856	4	0	_uhttps://doi.org/10.1007/978-3-030-93088-2 _3Springer eBooks _zOnline access link to the resource
942			_2lcc _cEBK